H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 4,246 184 World Ranking 8757 National Ranking 810

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Computer network

Artificial intelligence, Computer vision, Feature extraction, Data compression and Pixel are his primary areas of study. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Embedded system and Pattern recognition. His Pattern recognition research is multidisciplinary, relying on both Image processing, Image and Uncompressed video.

His work in Computer vision is not limited to one particular discipline; it also encompasses Video quality. His work carried out in the field of Feature extraction brings together such families of science as Feature, Object detection, Pairwise comparison, Kernel and Scaling. His studies deal with areas such as Video compression picture types, Kadir–Brady saliency detector, Image quality and Human visual system model as well as Data compression.

His most cited work include:

  • Saliency Detection in the Compressed Domain for Adaptive Image Retargeting (260 citations)
  • A Video Saliency Detection Model in Compressed Domain (167 citations)
  • Multi-level Fusion Based 3D Object Detection from Monocular Images (159 citations)

What are the main themes of his work throughout his whole career to date?

Zhenzhong Chen mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Feature extraction. Zhenzhong Chen performs multidisciplinary studies into Artificial intelligence and Distortion in his work. As part of the same scientific family, he usually focuses on Computer vision, concentrating on Video quality and intersecting with Scalable Video Coding and Computer network.

His Pattern recognition research includes elements of Frame, Representation, Feature and Salience. His work on Computational complexity theory and Quantization is typically connected to Block as part of general Algorithm study, connecting several disciplines of science. His Feature extraction study frequently links to adjacent areas such as Object detection.

He most often published in these fields:

  • Artificial intelligence (59.64%)
  • Computer vision (38.12%)
  • Pattern recognition (19.73%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (59.64%)
  • Feature extraction (12.11%)
  • Pattern recognition (19.73%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Artificial intelligence, Feature extraction, Pattern recognition, Convolutional neural network and Computer vision. His study brings together the fields of Frame and Artificial intelligence. Zhenzhong Chen interconnects Visualization, Video quality and Information retrieval in the investigation of issues within Feature extraction.

His work deals with themes such as Decoding methods, Aggregate and Encoding, which intersect with Pattern recognition. The Convolutional neural network study combines topics in areas such as Segmentation, Classifier, Inhibition of return, Human eye and Remote sensing. Zhenzhong Chen integrates many fields, such as Computer vision and Asymmetry, in his works.

Between 2019 and 2021, his most popular works were:

  • Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation (20 citations)
  • MonoFENet: Monocular 3D Object Detection With Feature Enhancement Networks (20 citations)
  • Learned Image Downscaling for Upscaling Using Content Adaptive Resampler (19 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Algorithm

Zhenzhong Chen spends much of his time researching Artificial intelligence, Artificial neural network, Pixel, Convolutional neural network and Feature extraction. His Artificial intelligence research incorporates elements of Computer vision and Pattern recognition. His biological study spans a wide range of topics, including Classifier and Semantic labeling.

His research investigates the connection between Pixel and topics such as Interpolation that intersect with issues in Motion interpolation, Optical flow and Image scaling. The various areas that he examines in his Convolutional neural network study include Remote sensing, Segmentation, Feature learning and Random forest. His Feature extraction research integrates issues from Visualization and Eye tracking.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Saliency Detection in the Compressed Domain for Adaptive Image Retargeting

Yuming Fang;Zhenzhong Chen;Weisi Lin;Chia-Wen Lin.
IEEE Transactions on Image Processing (2012)

310 Citations

Perceptually-Friendly H.264/AVC Video Coding Based on Foveated Just-Noticeable-Distortion Model

Zhenzhong Chen;Christine Guillemot.
IEEE Transactions on Circuits and Systems for Video Technology (2010)

226 Citations

QoE-Driven Cache Management for HTTP Adaptive Bit Rate Streaming Over Wireless Networks

Weiwen Zhang;Yonggang Wen;Zhenzhong Chen;A. Khisti.
IEEE Transactions on Multimedia (2013)

220 Citations

QoE-driven cache management for HTTP adaptive bit rate (ABR) streaming over wireless networks

Weiwen Zhang;Yonggang Wen;Zhenzhong Chen;Ashish Khisti.
global communications conference (2012)

218 Citations

A Video Saliency Detection Model in Compressed Domain

Yuming Fang;Weisi Lin;Zhenzhong Chen;Chia-Ming Tsai.
IEEE Transactions on Circuits and Systems for Video Technology (2014)

212 Citations

Multi-level Fusion Based 3D Object Detection from Monocular Images

Bin Xu;Zhenzhong Chen.
computer vision and pattern recognition (2018)

205 Citations

Recent advances in rate control for video coding

Zhenzhong Chen;King Ngi Ngan.
Signal Processing-image Communication (2007)

175 Citations

Bottom-Up Saliency Detection Model Based on Human Visual Sensitivity and Amplitude Spectrum

Yuming Fang;Weisi Lin;Bu-Sung Lee;Chiew-Tong Lau.
IEEE Transactions on Multimedia (2012)

163 Citations

Binocular Just-Noticeable-Difference Model for Stereoscopic Images

Yin Zhao;Zhenzhong Chen;Ce Zhu;Yap-Peng Tan.
IEEE Signal Processing Letters (2011)

126 Citations

Assessing Visual Quality of Omnidirectional Videos

Mai Xu;Chen Li;Zhenzhong Chen;Zulin Wang.
IEEE Transactions on Circuits and Systems for Video Technology (2019)

105 Citations

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